Covid 19 situation hospitalière depuis 2020-03-07.

Importation des librairies

In [1]:
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
In [3]:
import ssl
ssl._create_default_https_context = ssl._create_unverified_context

Etude pour 100 000 habitants

In [5]:
df = pd.read_csv('https://www.data.gouv.fr/fr/datasets/r/e3d83ab3-dc52-4c99-abaf-8a38050cc68c',sep=';')
In [6]:
df.head()
Out[6]:
fra jour clage_90 PourAvec tx_indic_7J_DC tx_indic_7J_hosp tx_indic_7J_SC tx_prev_hosp tx_prev_SC
0 FR 2020-03-07 0 0 0.0 0.0 0.0 1.168144 0.143038
1 FR 2020-03-07 0 1 NaN 0.0 0.0 0.000000 0.000000
2 FR 2020-03-07 0 2 NaN 0.0 0.0 0.000000 0.000000
3 FR 2020-03-08 0 0 0.0 0.0 0.0 1.302243 0.174328
4 FR 2020-03-08 0 1 NaN 0.0 0.0 0.000000 0.000000
In [7]:
df.tail()
Out[7]:
fra jour clage_90 PourAvec tx_indic_7J_DC tx_indic_7J_hosp tx_indic_7J_SC tx_prev_hosp tx_prev_SC
23458 FR 2022-02-14 90 1 NaN 121.961829 3.506540 380.788281 4.711913
23459 FR 2022-02-14 90 2 NaN 45.913752 1.095794 177.189827 1.534111
23460 FR 2022-02-15 90 0 43.174268 157.246384 4.931071 546.472275 6.465182
23461 FR 2022-02-15 90 1 NaN 116.702020 3.944857 373.227305 5.150230
23462 FR 2022-02-15 90 2 NaN 40.544364 0.986214 173.244970 1.314952

Description

tx_indic_7J_DC = Taux de personnes décédées durant les 7 derniers jours (pour 100 000 hab.)
tx_indic_7J_hosp = Taux de nouvelles hospitalisation lors des 7 derniers jours (pour 100 000 hab.)
tx_indic_7J_SC = Taux de nouvelles hospitalisation en soins critiques lors des 7 derniers jours (pour 100 000 hab.)
tx_prev_hosp = Taux personnes hospitalisées (pour 100 000 hab.)
tx_prev_SC = Taux de personnes en soins critiques (pour 100 000 hab.)
cl_age90 = classe d'age

pouravec :
0 = Hospitalisations avec infection SARS-CoV-2 (quelque soit le motif de l'hospitalisation)
1 = Hospitalisations pour COVID-19
2 = Hospitalisations pour autre motif, avec infection SARS-CoV-2

In [8]:
df.shape
Out[8]:
(23463, 9)
In [9]:
df.dtypes
Out[9]:
fra                  object
jour                 object
clage_90              int64
PourAvec              int64
tx_indic_7J_DC      float64
tx_indic_7J_hosp    float64
tx_indic_7J_SC      float64
tx_prev_hosp        float64
tx_prev_SC          float64
dtype: object
In [10]:
print(f"Nombre duplicats : {len(df) - len(df.drop_duplicates())}")

Nombre duplicats : 0
In [11]:
df['jour'] = pd.to_datetime(df['jour'])
In [12]:
df['fra'].value_counts()/len(df)
Out[12]:
FR    1.0
Name: fra, dtype: float64
In [13]:
df['fra'] = df['fra'].astype('str')
In [14]:
df.dtypes
Out[14]:
fra                         object
jour                datetime64[ns]
clage_90                     int64
PourAvec                     int64
tx_indic_7J_DC             float64
tx_indic_7J_hosp           float64
tx_indic_7J_SC             float64
tx_prev_hosp               float64
tx_prev_SC                 float64
dtype: object

starting DataViz

In [15]:
header=['pays','date','classeAge','HopitaliséPour','décès_en_7j','hospitalisations_en_7j','soinsCritiques_en_7j','Taux_hospitalisés','Taux_soinsCritiques']
df.columns=header
df.head()
Out[15]:
pays date classeAge HopitaliséPour décès_en_7j hospitalisations_en_7j soinsCritiques_en_7j Taux_hospitalisés Taux_soinsCritiques
0 FR 2020-03-07 0 0 0.0 0.0 0.0 1.168144 0.143038
1 FR 2020-03-07 0 1 NaN 0.0 0.0 0.000000 0.000000
2 FR 2020-03-07 0 2 NaN 0.0 0.0 0.000000 0.000000
3 FR 2020-03-08 0 0 0.0 0.0 0.0 1.302243 0.174328
4 FR 2020-03-08 0 1 NaN 0.0 0.0 0.000000 0.000000
In [16]:
corr=df.corr()
plt.figure(figsize=(16, 12))
ax=sns.heatmap(corr,
            xticklabels=corr.columns,
            yticklabels=corr.columns,
            cmap=sns.diverging_palette(220, 10, as_cmap=True),
            square=True,
            linewidths=.1)
ax.set(title="corrélation entre les variables");
In [17]:
fig,ax = plt.subplots(figsize = (11,7))

ax.grid(ls="dotted", lw="0.5", color="w", zorder=1)

hfont = {'fontname':'Impact'}

fig.set_facecolor("#08143D")
ax.patch.set_facecolor("#08143D")

ax.tick_params(colors="w", length=15)

ax.spines['bottom'].set_color("w")
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_color("w")

plt.plot(df['date'], df['Taux_hospitalisés'], zorder=0, color="#2bb6e0", label = "taux d'hospitalisés")
plt.plot(df['date'], df['Taux_soinsCritiques'], zorder=0, color="#8BF600", label = "taux soins critiques")

plt.xlabel("date", color="w", fontsize=15 , **hfont)
plt.ylabel("Nombre pour 100 000 habitants", color="w", fontsize=15 , **hfont)

fig.text(0.12,0.97, s="Evolution des hospitalisations et des soins critiques\n", fontsize = 16, fontweight = "bold", color = "w", **hfont)
fig.text(0.12,0.9, "pour 100 000 habitants", fontsize = 12, fontweight="light", color = "w", **hfont)

plt.legend(loc='upper left')

plt.show()

C:\Users\grego\Anaconda3\lib\site-packages\pandas\plotting\_matplotlib\converter.py:103: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.

To register the converters:
	>>> from pandas.plotting import register_matplotlib_converters
	>>> register_matplotlib_converters()
  warnings.warn(msg, FutureWarning)
In [18]:
fig,ax = plt.subplots(figsize = (25,7))

ax.grid(ls="dotted", lw="0.5", color="w", zorder=1)

hfont = {'fontname':'Impact'}

fig.set_facecolor("#08143D")
ax.patch.set_facecolor("#08143D")

ax.tick_params(colors="w", length=15)

ax.spines['bottom'].set_color("w")
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_color("w")


df.groupby('classeAge').mean()["Taux_soinsCritiques"].plot(kind='bar', zorder=0,width = 0.9, color="#2bb6e0", label = "taux d'hospitalisés")

plt.xlabel("classe d'âge", color="w", fontsize=15 , **hfont)
plt.ylabel("Nombre pour 100 000 habitants", color="w", fontsize=15 , **hfont)

fig.text(0.12,0.97, s="Répartitions des soins critiques par classe d'âges\n", fontsize = 16, fontweight = "bold", color = "w", **hfont)
fig.text(0.12,0.9, "pour 100 000 habitants", fontsize = 12, fontweight="light", color = "w", **hfont)

plt.show()

Etude globale

In [19]:
df2 = pd.read_csv('https://www.data.gouv.fr/fr/datasets/r/5c4e1452-3850-4b59-b11c-3dd51d7fb8b5',sep=',')

C:\Users\grego\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3058: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)
In [20]:
df2.head()
Out[20]:
dep date reg lib_dep lib_reg tx_pos tx_incid TO R hosp ... dchosp reg_rea incid_hosp incid_rea incid_rad incid_dchosp reg_incid_rea pos pos_7j cv_dose1
0 01 2020-03-18 84 Ain Auvergne et Rhône-Alpes NaN NaN 0.062612 NaN 2 ... 0 35 NaN NaN NaN NaN NaN NaN NaN NaN
1 01 2020-03-19 84 Ain Auvergne et Rhône-Alpes NaN NaN 0.132379 NaN 2 ... 0 79 1.0 0.0 0.0 0.0 44.0 NaN NaN NaN
2 01 2020-03-20 84 Ain Auvergne et Rhône-Alpes NaN NaN 0.155635 NaN 2 ... 0 87 0.0 0.0 1.0 0.0 16.0 NaN NaN NaN
3 01 2020-03-21 84 Ain Auvergne et Rhône-Alpes NaN NaN 0.173524 NaN 4 ... 0 88 3.0 0.0 0.0 0.0 15.0 NaN NaN NaN
4 01 2020-03-22 84 Ain Auvergne et Rhône-Alpes NaN NaN 0.212880 NaN 8 ... 0 106 3.0 1.0 1.0 0.0 25.0 NaN NaN NaN

5 rows × 22 columns

In [21]:
df2.dtypes
Out[21]:
dep               object
date              object
reg                int64
lib_dep           object
lib_reg           object
tx_pos           float64
tx_incid         float64
TO               float64
R                float64
hosp               int64
rea                int64
rad                int64
dchosp             int64
reg_rea            int64
incid_hosp       float64
incid_rea        float64
incid_rad        float64
incid_dchosp     float64
reg_incid_rea    float64
pos              float64
pos_7j           float64
cv_dose1         float64
dtype: object
In [22]:
df2['date'] = pd.to_datetime(df2['date'])
In [23]:
df2['dep'].unique()
Out[23]:
array(['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11',
       '12', '13', '14', '15', '16', '17', '18', '19', '21', '22', '23',
       '24', '25', '26', '27', '28', '29', '2A', '2B', '30', '31', '32',
       '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43',
       '44', '45', '46', 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57,
       58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74,
       75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
       92, 93, 94, 95, 971, 972, 973, 974, 976], dtype=object)
In [24]:
df2 = df2[df2['dep'] != '2A']
df2 = df2[df2['dep'] != '2B']
df2 = df2[df2['dep'] != 971]
df2 = df2[df2['dep'] != 972]
df2 = df2[df2['dep'] != 973]
df2 = df2[df2['dep'] != 974]
df2 = df2[df2['dep'] != 976]
In [25]:
df2['dep'].unique()
Out[25]:
array(['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11',
       '12', '13', '14', '15', '16', '17', '18', '19', '21', '22', '23',
       '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34',
       '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45',
       '46', 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
       61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,
       78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94,
       95], dtype=object)
In [26]:
df2['dep']=df2['dep'].astype(int)
In [27]:
df2['dep'].unique()
Out[27]:
array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,
       53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
       70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86,
       87, 88, 89, 90, 91, 92, 93, 94, 95], dtype=int64)
In [28]:
df2.shape
Out[28]:
(66176, 22)
In [29]:
import plotly.express as px
import requests

france = requests.get('https://france-geojson.gregoiredavid.fr/repo/departements.geojson').json()


departements = {}
for feature in france["features"]:
    feature["Departement"] = feature["properties"]["code"]
    departements[feature["properties"]["nom"]] = feature["Departement"]
departements = (sorted(departements.items(), key=lambda t: t[1]))


donneeGeoFrance = pd.DataFrame(departements)
donneeGeoFrance.columns = ['Nom departement','Code departement']

donneeGeoFrance
Out[29]:
Nom departement Code departement
0 Ain 01
1 Aisne 02
2 Allier 03
3 Alpes-de-Haute-Provence 04
4 Hautes-Alpes 05
... ... ...
91 Essonne 91
92 Hauts-de-Seine 92
93 Seine-Saint-Denis 93
94 Val-de-Marne 94
95 Val-d'Oise 95

96 rows × 2 columns

In [30]:
donneeGeoFrance.dtypes
Out[30]:
Nom departement     object
Code departement    object
dtype: object
In [31]:
donneeGeoFrance['Code departement'].unique()
Out[31]:
array(['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11',
       '12', '13', '14', '15', '16', '17', '18', '19', '21', '22', '23',
       '24', '25', '26', '27', '28', '29', '2A', '2B', '30', '31', '32',
       '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43',
       '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54',
       '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65',
       '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76',
       '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87',
       '88', '89', '90', '91', '92', '93', '94', '95'], dtype=object)
In [32]:
donneeGeoFrance = donneeGeoFrance[donneeGeoFrance['Code departement'] != '2A']
donneeGeoFrance = donneeGeoFrance[donneeGeoFrance['Code departement'] != '2B']

donneeGeoFrance['Code departement']=donneeGeoFrance['Code departement'].astype(int)

donneeGeoFrance['Code departement'].unique()
Out[32]:
array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,
       53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
       70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86,
       87, 88, 89, 90, 91, 92, 93, 94, 95], dtype=int64)
In [33]:
donneeGeoFrance
Out[33]:
Nom departement Code departement
0 Ain 1
1 Aisne 2
2 Allier 3
3 Alpes-de-Haute-Provence 4
4 Hautes-Alpes 5
... ... ...
91 Essonne 91
92 Hauts-de-Seine 92
93 Seine-Saint-Denis 93
94 Val-de-Marne 94
95 Val-d'Oise 95

94 rows × 2 columns

Création de la carte

In [34]:
df_region=df2.groupby(by=["dep"]).mean().reset_index().sort_values(by=["dep"],ascending=True)
df_region
Out[34]:
dep reg tx_pos tx_incid TO R hosp rea rad dchosp reg_rea incid_hosp incid_rea incid_rad incid_dchosp reg_incid_rea pos pos_7j cv_dose1
0 1 84.0 7.929709 377.547577 0.538164 1.111835 129.085227 10.981534 1855.771307 409.015625 300.825284 6.789474 0.981508 5.504979 1.076814 22.782361 354.474419 2480.317684 74.2
1 2 32.0 6.677147 288.815915 0.575333 1.090092 152.328125 19.740057 2809.995739 728.436080 251.984375 10.177809 1.357041 7.934566 1.856330 17.226174 218.074419 1519.316119 79.5
2 3 84.0 5.864608 271.270713 0.538164 1.111835 73.910511 8.360795 1353.833807 363.163352 300.825284 5.813656 0.597440 4.472262 1.027027 22.782361 129.410853 898.760563 82.1
3 4 93.0 6.307779 337.142879 0.597743 1.086239 67.941761 4.157670 739.170455 152.134943 274.947443 3.322902 0.251778 2.314367 0.475107 18.977240 79.979845 556.949922 72.0
4 5 93.0 7.452661 367.231560 0.597743 1.086239 59.953125 6.345170 800.933239 161.170455 274.947443 3.467994 0.490754 2.743954 0.472262 18.977240 74.420155 520.572770 77.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
89 91 11.0 7.317378 370.392507 0.627582 1.062018 465.001420 72.461648 6490.715909 1292.589489 719.754261 23.280228 4.140825 18.745377 3.337127 47.729730 696.195349 4886.962441 74.7
90 92 11.0 6.028384 386.480363 0.627582 1.062018 737.883523 111.995739 10282.782670 1971.414773 719.754261 34.495021 7.532006 27.682788 4.704125 47.729730 890.463566 6236.873239 78.8
91 93 11.0 7.763519 385.311456 0.627582 1.062018 676.242898 82.083807 9313.294034 1802.613636 719.754261 32.083926 5.769559 25.209104 4.233286 47.729730 917.037209 6435.275430 66.8
92 94 11.0 7.096531 397.549497 0.627582 1.062018 613.285511 79.535511 9700.855114 2166.484375 719.754261 33.852063 6.240398 26.561878 5.088193 47.729730 797.941085 5589.708920 74.5
93 95 11.0 7.788135 407.956665 0.627582 1.062018 387.428977 41.163352 6434.670455 1436.325284 719.754261 22.217639 2.604552 18.014225 3.544808 47.729730 726.062016 5092.743349 73.6

94 rows × 19 columns

In [35]:
donneeGeoFranceF=pd.merge(donneeGeoFrance, df_region, left_on='Code departement', right_on='dep')
donneeGeoFranceF
Out[35]:
Nom departement Code departement dep reg tx_pos tx_incid TO R hosp rea ... dchosp reg_rea incid_hosp incid_rea incid_rad incid_dchosp reg_incid_rea pos pos_7j cv_dose1
0 Ain 1 1 84.0 7.929709 377.547577 0.538164 1.111835 129.085227 10.981534 ... 409.015625 300.825284 6.789474 0.981508 5.504979 1.076814 22.782361 354.474419 2480.317684 74.2
1 Aisne 2 2 32.0 6.677147 288.815915 0.575333 1.090092 152.328125 19.740057 ... 728.436080 251.984375 10.177809 1.357041 7.934566 1.856330 17.226174 218.074419 1519.316119 79.5
2 Allier 3 3 84.0 5.864608 271.270713 0.538164 1.111835 73.910511 8.360795 ... 363.163352 300.825284 5.813656 0.597440 4.472262 1.027027 22.782361 129.410853 898.760563 82.1
3 Alpes-de-Haute-Provence 4 4 93.0 6.307779 337.142879 0.597743 1.086239 67.941761 4.157670 ... 152.134943 274.947443 3.322902 0.251778 2.314367 0.475107 18.977240 79.979845 556.949922 72.0
4 Hautes-Alpes 5 5 93.0 7.452661 367.231560 0.597743 1.086239 59.953125 6.345170 ... 161.170455 274.947443 3.467994 0.490754 2.743954 0.472262 18.977240 74.420155 520.572770 77.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
89 Essonne 91 91 11.0 7.317378 370.392507 0.627582 1.062018 465.001420 72.461648 ... 1292.589489 719.754261 23.280228 4.140825 18.745377 3.337127 47.729730 696.195349 4886.962441 74.7
90 Hauts-de-Seine 92 92 11.0 6.028384 386.480363 0.627582 1.062018 737.883523 111.995739 ... 1971.414773 719.754261 34.495021 7.532006 27.682788 4.704125 47.729730 890.463566 6236.873239 78.8
91 Seine-Saint-Denis 93 93 11.0 7.763519 385.311456 0.627582 1.062018 676.242898 82.083807 ... 1802.613636 719.754261 32.083926 5.769559 25.209104 4.233286 47.729730 917.037209 6435.275430 66.8
92 Val-de-Marne 94 94 11.0 7.096531 397.549497 0.627582 1.062018 613.285511 79.535511 ... 2166.484375 719.754261 33.852063 6.240398 26.561878 5.088193 47.729730 797.941085 5589.708920 74.5
93 Val-d'Oise 95 95 11.0 7.788135 407.956665 0.627582 1.062018 387.428977 41.163352 ... 1436.325284 719.754261 22.217639 2.604552 18.014225 3.544808 47.729730 726.062016 5092.743349 73.6

94 rows × 21 columns

In [36]:
fig = px.choropleth(data_frame=donneeGeoFranceF, 
                    geojson=france, 
                    locations='Nom departement',
                    featureidkey='properties.nom',
                    color='rea',
                    color_continuous_scale="Reds",
                    hover_name="Nom departement",
                    hover_data=["rea"],
                    title="Nombre de réa en moyenne par département sur toute la période",
                   )
fig.update_geos(showcountries=False, showcoastlines=False, showland=False, fitbounds="locations")

fig
In [37]:
df_hospi=df2.groupby(by=["date"]).sum().reset_index().sort_values(by=["date"],ascending=True)
df_hospi
Out[37]:
date dep reg tx_pos tx_incid TO R hosp rea rad dchosp reg_rea incid_hosp incid_rea incid_rad incid_dchosp reg_incid_rea pos pos_7j cv_dose1
0 2020-03-18 4540 5114 0.000000 0.000000 12.176066 0.0 2954 764 816 217 6468 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 2020-03-19 4540 5114 0.000000 0.000000 18.068292 0.0 4053 995 1179 325 8499 2168.0 427.0 509.0 154.0 3644.0 0.0 0.0 0.0
2 2020-03-20 4540 5114 0.000000 0.000000 20.742638 0.0 5139 1276 1554 446 10800 1218.0 242.0 294.0 81.0 2048.0 0.0 0.0 0.0
3 2020-03-21 4540 5114 0.000000 0.000000 24.615065 0.0 5811 1432 1776 520 12120 1536.0 298.0 365.0 114.0 2489.0 0.0 0.0 0.0
4 2020-03-22 4540 5114 0.000000 0.000000 27.966277 0.0 6863 1648 2081 625 13973 1520.0 301.0 314.0 122.0 2580.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
699 2022-02-15 4540 5114 2795.952325 111269.817232 57.501412 0.0 29852 3077 524953 104311 26343 2369.0 276.0 2493.0 286.0 2433.0 86621.0 693286.0 0.0
700 2022-02-16 4540 5114 2734.026862 102348.165520 56.003784 0.0 29436 2974 527020 104542 25561 1925.0 197.0 2081.0 232.0 1760.0 84551.0 637887.0 0.0
701 2022-02-17 4540 5114 0.000000 0.000000 54.900839 0.0 28757 2907 529221 104788 24989 1797.0 222.0 2223.0 247.0 1933.0 0.0 0.0 7457.9
702 2022-02-18 4540 5114 0.000000 0.000000 53.686112 0.0 28137 2828 531267 105040 24337 1727.0 187.0 2084.0 252.0 1580.0 0.0 0.0 0.0
703 2022-02-19 4540 5114 0.000000 0.000000 52.716674 0.0 27575 2783 532603 105178 23858 939.0 121.0 1344.0 138.0 1022.0 0.0 0.0 0.0

704 rows × 20 columns

In [38]:
fig,ax = plt.subplots(figsize = (11,7))

ax.grid(ls="dotted", lw="0.5", color="w", zorder=1)

hfont = {'fontname':'Impact'}

fig.set_facecolor("#08143D")
ax.patch.set_facecolor("#08143D")

ax.tick_params(colors="w", length=15)

ax.spines['bottom'].set_color("w")
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_color("w")

plt.plot(df_hospi['date'], df_hospi['hosp'], zorder=0, color="#2bb6e0", label = "hospitalissation")
plt.plot(df_hospi['date'], df_hospi['rea'], zorder=0, color="#8BF600", label = "réanimations")

plt.fill_between(df_hospi['date'], df_hospi['hosp'], color='#2bb6e0')
plt.fill_between(df_hospi['date'], df_hospi['rea'], color='#8BF600')


plt.xlabel("date", color="w", fontsize=15 , **hfont)
plt.ylabel("Nombre de personnes", color="w", fontsize=15 , **hfont)

fig.text(0.12,0.97, s="Evolution des hospitalisations et des soins critiques\n", fontsize = 16, fontweight = "bold", color = "w", **hfont)

plt.legend(loc='upper left')

plt.show()
In [39]:
fig,ax = plt.subplots(figsize = (11,7))

ax.grid(ls="dotted", lw="0.5", color="w", zorder=1)

hfont = {'fontname':'Impact'}

fig.set_facecolor("#08143D")
ax.patch.set_facecolor("#08143D")

ax.tick_params(colors="w", length=15)

ax.spines['bottom'].set_color("w")
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_color("w")

plt.plot(df_hospi['date'], df_hospi['incid_hosp'], zorder=0, color="#2bb6e0", label = "hospitalisation")
plt.plot(df_hospi['date'], df_hospi['incid_rad'], zorder=0, color="#8BF600", label = "réanimations")

plt.fill_between(df_hospi['date'], df_hospi['incid_hosp'], color='#2bb6e0')
plt.fill_between(df_hospi['date'], df_hospi['incid_rad'], color='#8BF600')


plt.xlabel("date", color="w", fontsize=15 , **hfont)
plt.ylabel("Nombre de personnes", color="w", fontsize=15 , **hfont)

fig.text(0.12,0.97, s="Evolution des hospitalisations et des retours à domiciles\n", fontsize = 16, fontweight = "bold", color = "w", **hfont)

plt.legend(loc='upper left')

plt.show()

Situation au 19 février 2022

In [40]:
df2.tail()
Out[40]:
dep date reg lib_dep lib_reg tx_pos tx_incid TO R hosp ... dchosp reg_rea incid_hosp incid_rea incid_rad incid_dchosp reg_incid_rea pos pos_7j cv_dose1
67579 95 2022-02-15 11 Val-d'Oise Île-de-France 17.097085 610.403780 0.612903 NaN 423 ... 2452 703 11.0 0.0 27.0 5.0 54.0 923.0 7620.0 NaN
67580 95 2022-02-16 11 Val-d'Oise Île-de-France 16.288584 567.707557 0.585876 NaN 410 ... 2457 672 28.0 1.0 33.0 5.0 22.0 956.0 7087.0 NaN
67581 95 2022-02-17 11 Val-d'Oise Île-de-France NaN NaN 0.557105 NaN 398 ... 2460 639 22.0 0.0 31.0 3.0 36.0 NaN NaN 73.6
67582 95 2022-02-18 11 Val-d'Oise Île-de-France NaN NaN 0.542284 NaN 388 ... 2461 622 12.0 0.0 21.0 1.0 27.0 NaN NaN NaN
67583 95 2022-02-19 11 Val-d'Oise Île-de-France NaN NaN 0.533566 NaN 391 ... 2462 612 20.0 2.0 17.0 1.0 22.0 NaN NaN NaN

5 rows × 22 columns

In [41]:
df_today = df2[df2['date'] == '2022-02-19']
In [42]:
df_today
Out[42]:
dep date reg lib_dep lib_reg tx_pos tx_incid TO R hosp ... dchosp reg_rea incid_hosp incid_rea incid_rad incid_dchosp reg_incid_rea pos pos_7j cv_dose1
703 1 2022-02-19 84 Ain Auvergne et Rhône-Alpes NaN NaN 0.567084 NaN 174 ... 730 317 9.0 1.0 13.0 4.0 12.0 NaN NaN NaN
1407 2 2022-02-19 32 Aisne Hauts-de-France NaN NaN 0.602740 NaN 270 ... 1301 264 14.0 2.0 2.0 5.0 20.0 NaN NaN NaN
2111 3 2022-02-19 84 Allier Auvergne et Rhône-Alpes NaN NaN 0.567084 NaN 150 ... 707 317 13.0 2.0 9.0 1.0 12.0 NaN NaN NaN
2815 4 2022-02-19 93 Alpes-de-Haute-Provence Provence-Alpes-Côte d'Azur NaN NaN 0.771739 NaN 142 ... 329 355 4.0 0.0 7.0 4.0 9.0 NaN NaN NaN
3519 5 2022-02-19 93 Hautes-Alpes Provence-Alpes-Côte d'Azur NaN NaN 0.771739 NaN 91 ... 332 355 8.0 0.0 6.0 1.0 9.0 NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
64767 91 2022-02-19 11 Essonne Île-de-France NaN NaN 0.533566 NaN 530 ... 2317 612 9.0 0.0 12.0 1.0 22.0 NaN NaN NaN
65471 92 2022-02-19 11 Hauts-de-Seine Île-de-France NaN NaN 0.533566 NaN 935 ... 3268 612 7.0 1.0 14.0 1.0 22.0 NaN NaN NaN
66175 93 2022-02-19 11 Seine-Saint-Denis Île-de-France NaN NaN 0.533566 NaN 866 ... 2943 612 42.0 7.0 53.0 2.0 22.0 NaN NaN NaN
66879 94 2022-02-19 11 Val-de-Marne Île-de-France NaN NaN 0.533566 NaN 844 ... 3530 612 21.0 5.0 14.0 1.0 22.0 NaN NaN NaN
67583 95 2022-02-19 11 Val-d'Oise Île-de-France NaN NaN 0.533566 NaN 391 ... 2462 612 20.0 2.0 17.0 1.0 22.0 NaN NaN NaN

94 rows × 22 columns

In [43]:
df_region_today=df_region.sort_values(by=["dep"],ascending=True)
df_region_today
Out[43]:
dep reg tx_pos tx_incid TO R hosp rea rad dchosp reg_rea incid_hosp incid_rea incid_rad incid_dchosp reg_incid_rea pos pos_7j cv_dose1
0 1 84.0 7.929709 377.547577 0.538164 1.111835 129.085227 10.981534 1855.771307 409.015625 300.825284 6.789474 0.981508 5.504979 1.076814 22.782361 354.474419 2480.317684 74.2
1 2 32.0 6.677147 288.815915 0.575333 1.090092 152.328125 19.740057 2809.995739 728.436080 251.984375 10.177809 1.357041 7.934566 1.856330 17.226174 218.074419 1519.316119 79.5
2 3 84.0 5.864608 271.270713 0.538164 1.111835 73.910511 8.360795 1353.833807 363.163352 300.825284 5.813656 0.597440 4.472262 1.027027 22.782361 129.410853 898.760563 82.1
3 4 93.0 6.307779 337.142879 0.597743 1.086239 67.941761 4.157670 739.170455 152.134943 274.947443 3.322902 0.251778 2.314367 0.475107 18.977240 79.979845 556.949922 72.0
4 5 93.0 7.452661 367.231560 0.597743 1.086239 59.953125 6.345170 800.933239 161.170455 274.947443 3.467994 0.490754 2.743954 0.472262 18.977240 74.420155 520.572770 77.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
89 91 11.0 7.317378 370.392507 0.627582 1.062018 465.001420 72.461648 6490.715909 1292.589489 719.754261 23.280228 4.140825 18.745377 3.337127 47.729730 696.195349 4886.962441 74.7
90 92 11.0 6.028384 386.480363 0.627582 1.062018 737.883523 111.995739 10282.782670 1971.414773 719.754261 34.495021 7.532006 27.682788 4.704125 47.729730 890.463566 6236.873239 78.8
91 93 11.0 7.763519 385.311456 0.627582 1.062018 676.242898 82.083807 9313.294034 1802.613636 719.754261 32.083926 5.769559 25.209104 4.233286 47.729730 917.037209 6435.275430 66.8
92 94 11.0 7.096531 397.549497 0.627582 1.062018 613.285511 79.535511 9700.855114 2166.484375 719.754261 33.852063 6.240398 26.561878 5.088193 47.729730 797.941085 5589.708920 74.5
93 95 11.0 7.788135 407.956665 0.627582 1.062018 387.428977 41.163352 6434.670455 1436.325284 719.754261 22.217639 2.604552 18.014225 3.544808 47.729730 726.062016 5092.743349 73.6

94 rows × 19 columns

In [44]:
donneeGeoFranceF_today=pd.merge(donneeGeoFrance, df_region_today, left_on='Code departement', right_on='dep')
donneeGeoFranceF_today
Out[44]:
Nom departement Code departement dep reg tx_pos tx_incid TO R hosp rea ... dchosp reg_rea incid_hosp incid_rea incid_rad incid_dchosp reg_incid_rea pos pos_7j cv_dose1
0 Ain 1 1 84.0 7.929709 377.547577 0.538164 1.111835 129.085227 10.981534 ... 409.015625 300.825284 6.789474 0.981508 5.504979 1.076814 22.782361 354.474419 2480.317684 74.2
1 Aisne 2 2 32.0 6.677147 288.815915 0.575333 1.090092 152.328125 19.740057 ... 728.436080 251.984375 10.177809 1.357041 7.934566 1.856330 17.226174 218.074419 1519.316119 79.5
2 Allier 3 3 84.0 5.864608 271.270713 0.538164 1.111835 73.910511 8.360795 ... 363.163352 300.825284 5.813656 0.597440 4.472262 1.027027 22.782361 129.410853 898.760563 82.1
3 Alpes-de-Haute-Provence 4 4 93.0 6.307779 337.142879 0.597743 1.086239 67.941761 4.157670 ... 152.134943 274.947443 3.322902 0.251778 2.314367 0.475107 18.977240 79.979845 556.949922 72.0
4 Hautes-Alpes 5 5 93.0 7.452661 367.231560 0.597743 1.086239 59.953125 6.345170 ... 161.170455 274.947443 3.467994 0.490754 2.743954 0.472262 18.977240 74.420155 520.572770 77.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
89 Essonne 91 91 11.0 7.317378 370.392507 0.627582 1.062018 465.001420 72.461648 ... 1292.589489 719.754261 23.280228 4.140825 18.745377 3.337127 47.729730 696.195349 4886.962441 74.7
90 Hauts-de-Seine 92 92 11.0 6.028384 386.480363 0.627582 1.062018 737.883523 111.995739 ... 1971.414773 719.754261 34.495021 7.532006 27.682788 4.704125 47.729730 890.463566 6236.873239 78.8
91 Seine-Saint-Denis 93 93 11.0 7.763519 385.311456 0.627582 1.062018 676.242898 82.083807 ... 1802.613636 719.754261 32.083926 5.769559 25.209104 4.233286 47.729730 917.037209 6435.275430 66.8
92 Val-de-Marne 94 94 11.0 7.096531 397.549497 0.627582 1.062018 613.285511 79.535511 ... 2166.484375 719.754261 33.852063 6.240398 26.561878 5.088193 47.729730 797.941085 5589.708920 74.5
93 Val-d'Oise 95 95 11.0 7.788135 407.956665 0.627582 1.062018 387.428977 41.163352 ... 1436.325284 719.754261 22.217639 2.604552 18.014225 3.544808 47.729730 726.062016 5092.743349 73.6

94 rows × 21 columns

In [45]:
donneeGeoFranceF_today.sum()
Out[45]:
Nom departement     AinAisneAllierAlpes-de-Haute-ProvenceHautes-Al...
Code departement                                                 4540
dep                                                              4540
reg                                                              5114
tx_pos                                                        587.629
tx_incid                                                      30412.6
TO                                                            43.4059
R                                                             103.489
hosp                                                            16549
rea                                                           2475.97
rad                                                            250478
dchosp                                                        55991.7
reg_rea                                                       20822.7
incid_hosp                                                    985.984
incid_rea                                                      172.22
incid_rad                                                     775.356
incid_dchosp                                                  151.393
reg_incid_rea                                                  1458.5
pos                                                           32087.3
pos_7j                                                         223773
cv_dose1                                                       7457.9
dtype: object

note : ne prend pas en compte la Corse et les DOM TOM

In [46]:
fig = px.choropleth(data_frame=donneeGeoFranceF_today, 
                    geojson=france, 
                    locations='Nom departement',
                    featureidkey='properties.nom',
                    color='rea',
                    color_continuous_scale="Reds",
                    hover_name="Nom departement",
                    hover_data=["rea"],
                    title="Nombre de réa par département au 19 février 2022",
                   )
fig.update_geos(showcountries=False, showcoastlines=False, showland=False, fitbounds="locations")

fig
In [47]:
fig = px.choropleth(data_frame=donneeGeoFranceF_today, 
                    geojson=france, 
                    locations='Nom departement',
                    featureidkey='properties.nom',
                    color='TO',
                    color_continuous_scale="Reds",
                    hover_name="Nom departement",
                    hover_data=["TO"],
                    title="Taux d'occupation au 19 février 2022",
                   )
fig.update_geos(showcountries=False, showcoastlines=False, showland=False, fitbounds="locations")

fig

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